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2018 The mpI act of Valuation Methods on the Likelihood of Mergers and Acquisitions of High- tech Startup Companies in Nigeria Anthony Okafor Walden University
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Walden University
College of Management and Technology
This is to certify that the doctoral dissertation by
Anthony Okafor
has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made.
Review Committee Dr. Mohammad Sharifzadeh, Committee Chairperson, Management Faculty Dr. Javier Fadul, Committee Member, Management Faculty Dr. Craig Barton, University Reviewer, Management Faculty
Chief Academic Officer Eric Riedel, Ph.D.
Walden University 2018
Abstract
The Impact of Valuation Methods on the Likelihood of Mergers and Acquisitions of High-tech
Startup Companies in Nigeria
by
Anthony Okafor
MS, Ladoke Akintola University of Technology, Ogbomoso, Nigeria, 2012
BS, Federal Polytechnic, Ado-Ekiti, Nigeria, 2004
Dissertation Submitted in Partial Fulfillment
of the Requirements for the Degree of
Doctor of Philosophy
Finance
Walden University
July 2018
Abstract
Valuing high-tech startups using traditional valuation models has continued to pose valuation challenges to entrepreneurs, investors as well as financial analysts. The complications in valuing startups are heightened by the variations in valuation methodologies and the absence of operational data. Identifying the appropriate methodology for valuing startups is crucial to establishing value and a prerequisite for accessing funding through mergers or acquisitions. The purpose of this study was to examine the effect of valuation methods on the likelihood of mergers and acquisitions of high-tech startup organizations in the Nigerian capital market. The theoretical underpinning of this study is rooted in valuation theory and mergers and acquisitions theories. The extent to which valuation methods impact the likelihood of securing funds through mergers and acquisitions was the overarching research question. Random sampling was used to obtain records of valuation methods and mergers and acquisitions that occurred between 2006 and 2016 from companies in the high-tech sector. A binary logistic regression model was used to test the impact of valuation methods on the likelihood of mergers and acquisitions of high-tech startups. The impact of valuation methods on the likelihood of mergers and acquisitions was found to be not statistically significant. The participants indicated a preference for specific valuation methods during negotiations for mergers and acquisitions. The findings have implications for positive social change via a reduction in the unemployment rate by encouraging startups with their innovation and entrepreneurship. This should help to facilitate the emergence of sound valuation methods for valuing high-tech startups in the Nigerian capital market.
The Impact of Valuation Methods on the Likelihood of Mergers and Acquisitions of High-tech
Startup Companies in Nigeria
by
Anthony Okafor
MS, Ladoke Akintola University of Technology, Ogbomoso Nigeria, 2012
BS, Federal Polytechnic, Ado Ekiti, Nigeria, 2004
Dissertation Submitted in Partial Fulfillment
of the Requirements for the Degree of
Doctor of Philosophy
Finance
Walden University
July 2018
Dedication
This work is dedicated my parents, Mr. Michael Okafor and late Mrs. Theresa Okafor for their enormous contribution towards my education, who through their enchantment for education and passion for excellence encouraged me to be ruthless in the pursuit of my goals. I extend a special dedication to my darling wife, Olusola for her pep talks, love, and unconditional support.
Thank you for believing and having confidence in my ability. To my children, Kamsy and
Daniel, thank you for your patience, understanding, and love. Knowing I have your support throughout the doctoral journey meant the whole world to me.
I wish to express my profound gratitude to my siblings, Bridget, Eucharia, Ben, Michael
Jr, Ejike, and Catherine. Your understanding is one thing I could be sure of, and that kept me going, you are definitely my fortress. To my professional associates and friends who have endured this period and stayed the course, I appreciate your encouragement, admonition and the incredible support dispensed. I would forever be indebted for your relentless support and friendship.
Acknowledgement
This dissertation would not have been possible without the guidance and reinforcement provided by so many people. I graciously acknowledge the contributions of my mentor and
Chair, Dr. Mohammad Sharifzadeh for his patience, wisdom, and leadership. The dexterity with which he managed the dissertation process beginning from birthing the study’s title to completing the study is remarkable. I want to particularly recognize Dr. Javier Fadul, my second committee member, and Dr. Craig Barton, the university research reviewer for their contributions and efforts at ensuring the overall quality of this work. The contributions made by the committee objectified the saying that intellection is transcendent. They not only supported me, but they were also unrelenting in challenging my thought process, and I am much better for their efforts.
A well-deserved appreciation goes to Dr. Raghu Korrapati, the chief methodologist of the faculty, Dr. Richard Schuttler, and Dr. David Bouvin, all of the faculty of management for their guidance and invigoration, your advice assisted me to conclude this journey successfully. Next, I must directly acknowledge Dr. Stephen Ajinola, Dr. Ezenwayi Amaechi, Dipo Ajayi, Sesan
Sowore for nudging me on, your words of admonition helped to complete this dissertation. Dr.
Ajinola apprised me of the Ph.D. journey that it involved perpetual studying and servitude; the journey thingified your position. Special recognition must now go to my bosses and colleagues, past and present, for their endurance and understanding, your moral support and constructive criticism infused the energy needed to complete this journey.
Table of Contents
List of Tables ...... v
List of Figures ...... vi
List of Acronyms ...... vii
Chapter 1 ...... 1
Introduction to the Study ...... 1
Background ...... 2
Problem Statement ...... 3
Purpose of the Study ...... 5
Research Questions and Hypotheses ...... 5
Theoretical Framework for the Study ...... 7
Nature of the Study ...... 9
Definitions...... 11
Assumptions ...... 14
Scope and Delimitations ...... 15
Limitations ...... 16
Significance of the Study ...... 17
Contribution to Theory ...... 17
Contribution to Practice ...... 18
Implications for Social Change ...... 19
Summary ...... 20
Chapter 2: Literature Review ...... 22
i
Historical Perspective of the Startup Phenomenon ...... 22
Formation and Development of Startups ...... 24
Literature Search Strategy...... 26
Theoretical Foundation ...... 27
Literature Review Part 1: Startup Valuation Theories ...... 30
Capital Asset Pricing Model ...... 30
Single Factor Model ...... 33
Multifactor Model and APT...... 35
Income Approach-Discounted Cash Flow Method...... 39
The Free Cash Flow (FCF) Model ...... 40
Free Cash Flow to Equity ...... 42
Asset Based Valuation Model ...... 43
Adjusted Net Asset Method ...... 44
Relative Valuation Method ...... 48
Real Option Valuation Model (ROVM) ...... 51
Venture Capital Method ...... 54
Literature Review Part 2: Mergers and Acquisitions Theories ...... 56
Wealth Maximization Theory ...... 58
Turnaround Theory ...... 61
Successful Turnaround Acquisitions ...... 63
Information Asymmetry Theory ...... 64
Motives for Mergers and Acquisitions ...... 66
ii
Typical Mergers and Acquisitions Process ...... 68
Summary ...... 70
Chapter 3: Research Method ...... 73
Research Design and Rationale ...... 73
Research Questions and Hypotheses ...... 75
Methodology ...... 78
Population ...... 79
Sampling Procedure ...... 81
G-Power ...... 82
Procedures for Recruitment of Participants ...... 83
Field Test ...... 83
Instrumentation and Operationalization of Constructs ...... 85
Data Analysis Plan ...... 90
Threat to Study Validity...... 93
Threats to External Validity ...... 94
Threats to Internal Validity ...... 96
Ethical Procedures ...... 98
Summary ...... 99
Chapter 4: Results ...... 101
Data Collection ...... 102
Descriptive Statistics ...... 103
Treatment and Intervention Fidelity ...... 107
iii
Assumptions for Binary Logistic Regression ...... 107
Binary Logistic Regression Results ...... 109
Summary and Transition ...... 120
Chapter 5: Discussion, Conclusions, and Recommendations ...... 122
Overview ...... 122
Discussion and Interpretation of the Findings ...... 124
Possible Explanation for Non-significant Statistics ...... 129
Limitation of the Study ...... 132
Recommendation for Further Actions ...... 133
Implications...... 135
Conclusion ...... 137
References ...... 138
Appendix A: Questionnaire ...... 179
iv
List of Tables
Table 1. Life-cycle Model...... 25
Table 2. A Description of the Adjusted Net Asset Approach ...... 47
Table 3. Relationship Table ...... ….77
Table 4. Summary of Variable Data Collection.……………….………..……………….90
Table 5. Hypothesis Testing: Summary of Applied Statistical Tests……..……………...93
Table 6. Statistics Showing Participants’ Preference of Valuation Methods……………104
Table 7. Demographic Representation of Participants …………………...……………..105
Table 8. Descriptive Statistics of the Binary Logistic Regression Variables.…………...106
Table 9. Collinearity Statistics ……………..……………….………..……………...... 108
Table 10. Test of Reliability ….….………..……………….………..……………...... 109
Table 11. Overall Model Fit Statistics. ……..………………………………..……..……113
Table 12. Model Summary of the Binary Logistic Regression .……..…..…………….....114
Table 13. Classification Table of the Estimation Model…………………………...... 114
Table 14. Coefficient Estimates of the Logistic Regression Predicting the Likelihood of
Mergers and Acquisitions Based on the Model .…………….……..…..………..115
Table 15. Z-Statistics of Each Predictor’s Contribution to the Model .....……………....116
Table 16. Predicting Likelihood of Mergers and Acquisitions Based on Individual
Predictors ………………………………………………………………………..117
Table 17. Research Findings Interpretation Structure ……….……..…..…………….....127
v
List of Figures
Figure 1: Business valuation models ...... 30
Figure 2: A typical mergers and acquisitions process ...... 69
Figure 3: Classification plot for the model ...... 112
vi
List of Acronyms
ABM: Asset Based Method
APT: Arbitrage Pricing Theory
ATCON: Association of Telecommunication Companies of Nigeria
CAC: Corporate Affairs Commission
CAPM: Capital Asset Pricing Model
CBN: Central Bank of Nigeria
DCF: Discounted Cash Flow
EBITDA: Earnings before Interest Tax Depreciation and Amortization
ICT: Information Communication Technology
LCCI: Lagos Chambers of Commerce and Industry
MVM: Mixed Valuation Method
NBS: National Bureau of Statistics
NSE: Nigerian Stock Exchange
OR: Other Valuation Method
P/E: Price to Earnings
ROVM: Real Option Valuation Model
RVM: Relative Valuation Method
SEC: Securities and Exchange Commission
VC: Venture Capital
VCM: Venture Capital Method
WACC: Weighted Average Cost of Capital
vii
1 Chapter 1
Introduction to the Study
The impact of innovative technologies on the growth of startups across the globe has been unprecedented. In this study, I examined the impact of valuation methods on the likelihood of mergers and acquisitions of high-tech startups in Nigeria. Startups are generally defined as companies that support the growth of a nation’s economy, but usually with less than 10 years of operation and with a business model that thrives on the application of innovative technologies
(Kollmann, Stöckmann, Hensellek, & Kensbock, 2016).
The growth of startups in Nigeria has been occasioned by the advances in technology, such as global system mobile (GSM) technology, online payment systems, telemarketing, e- commerce, and so on. With the growing importance of high-tech firms and the positive impact that they have on the economy (Raymond, Moses, Ezenyirimba & Otugo, 2014), the valuation of high-tech firms has started to dominate the attention of financial analysts as well as scholars.
According to the National Bureau of Statistics (NBS) of Nigeria, the information and communications technology (ICT) sector contributed 10.40% to the gross domestic product
(GDP) of Nigeria in 2016. The role of startups in reducing unemployment among the youths and their contribution to the modernization of economies has been widely reported (Fitzgerald,
Haynes, Schrank, & Danes, 2010; Kritikos, 2014; Makinde, 2013; Poudyal, Siry, & Bowker,
2012).
The valuation of startups is becoming more and more important in today’s economy
(Flanc, 2014). Estimating the value of high-tech startups is still a relatively new subject of research. Marom and Lussier (2014) observed that over 50% of young businesses fail within 5
2 years of commencement due primarily to funding issues. Most entrepreneurs do not keep financial records making it difficult for entrepreneurs and investors to estimate the value of their firms or assess the value of their potential investments (Damodaran, 2012; Okeke & Eme, 2014).
In this chapter, I describe the purpose of the study, the research question and hypotheses and the problem statement. The theoretical framework relevant to the variables in the study is also discussed. Other sections addressed in this chapter include the assumptions made, the scope and delimitation of the study, as well as the significance of the research. The findings of this study are expected to contribute to positive social change by enhancing the knowledge base of entrepreneurs as well as investors.
Background
The growth in economic activities across the globe has led to an upsurge in entrepreneurial activities, with advances in innovative technologies leading the pack (Goutam &
Sarkar, 2015). Zhang and Zhang (2014) pointed out the major roles that startups play in the modernization and growth of any economy particularly the positive impact they have on innovation and alleviating the problems of unemployment. Marom and Lussier (2014) observed that over 50% of young businesses fail within 5 years of commencement, due primarily to funding issues. The challenge has been how to monetize the innovative tendencies of high-tech startups. An expectation gap exists between owners and investors because firm owners seek higher valuations while investors seek lower valuations, thus making the estimation of a startup’s value cumbersome (Halt, Donch, Stiles, & Fesnak, 2017).
Another challenge with determining the value of startups is the confusion as to the valuation method (model or approach) that best approximates the value of the firm (Cohen,
3 Diether, & Malloy, 2013). This confusion may be further amplified depending on the level of information asymmetry - the asymmetrical distribution of information between firm owners and investors (Stiglitz, 2000) that exists between owners of startups and investors (Fosu, Danso,
Ahmad, & Coffie, 2016). Because of the seemingly unclear valuation processes, Monika, Nitu, and Latika (2013) described as guesswork, the process of valuing startups. Funders and founders are also frustrated by the huge variance in estimates computed from the extant methods for the same new venture (Miloud, Aspelund & Cabrol, 2012).
Additionally, the emergence of high-tech industries such as semiconductors, biotechnology, the internet, e-commerce, health-care, including the emergence of several disruptive innovative technologies across the different sectors of the economy has continued to pose valuation challenges to venture capitals as well as investors (Nanda & Rhodes-Kropf,
2013). The existence of complex capital structures adds to the difficulty (Rob & Robinson,
2012). While various valuation methods exist, determining the specific value remains difficult due to the amount of uncertainty surrounding early stage companies (Aydin, 2015).
While prior researchers have focused on valuation methods applicable to mergers and acquisitions targets (Gompers, Kaplan & Mukharlyamov, 2016; Olbrich, Quill & Rapp, 2015;
Reddy, Agrawal, & Nangia, 2013; Söderblom, Samuelsson & Mårtensson, 2013), there is a lack of information about the relationship between valuation methods and the likelihood of consummating mergers and acquisitions.
Problem Statement
Determining the value of startups has been a contentious issue because of lack of historical data and many uncertain factors about the future of the organization (Festel,
4 Wuermseher & Cattaneo, 2013). Thousands of startups are acquired or sold every year leaving the founders or funders sometimes frustrated because of the variations in theoretical valuations and the practical propositions of the firm (Loukianova, Nikulin, & Vedernikov, 2017). The challenge with valuing startups is further heightened by the several valuation methods available and the many unknowns that characterize an innovative venture (Damodaran, 2012). The general problem is that, in spite of the various available valuation methods, valuing startups has become complicated, resulting in significant discounts in the purchase or sales value of between 20% and
40% compared with publicly traded companies (Aydin, 2015; Fazekas, 2016; Schootbrugge &
Wong, 2013).
A preponderance of literature exists in the field of finance that highlights the different aspects of firm valuation, and methods of firm valuation including the mergers and acquisitions processes (Loukianova, Nikulin, & Vedernikov, 2017). In the past decade, the attention of scholars has focused on the valuation methods used in the mergers and acquisitions process
(Dorisz, 2015; Rózsa, 2014; Safwan, 2016). Other scholars have investigated the challenges of using the cost method of valuation when making an investment decision (Onyejiaka, Oladejo, &
Emoh, (2015). The specific problem is that owners of startups, as well as investors, need to determine acceptable valuation methods that leads to securing funds through mergers and acquisitions in order to limit valuation challenges and to minimize the significant discounts paid on startups in the high-tech industry in Nigeria (Cohen, Diether, & Malloy, 2013; Mangipudi,
Subramanian & Vasu, 2013).
PriceWaterHouseCoopers (2012) noted that inability to agree on valuation had been the single most important cause of uncompleted mergers and acquisitions deals in Nigeria. The
5 mortality rate in the high-tech industry is estimated at 50% in the first 4-5 years of operation
(Hyytinen, Pajarinen, & Rouvinen, 2015). Consequently, identifying the effect of valuation methods on the likelihood of securing funding through mergers and acquisitions for startups may go a long way towards addressing the mortality rate of startups in the country and may also contribute to positive social change. Researchers have called for increased quantitative and qualitative analyses of acceptable valuation methods for startups in the high-tech industry to improve mergers and acquisitions activities in that sector (Chen, & Yang, 2014).
Purpose of the Study
The purpose of this quantitative survey study was to examine the effect of valuation methods on the likelihood of mergers and acquisitions of high-tech startup organizations in the
Nigerian capital market. The focus was to assess whether valuation methods do influence the chance of securing funds through mergers and acquisitions. The dependent variable was the likelihood of mergers and acquisitions of high-tech startups, while the independent variables were the valuation methods. Determining the association between valuation methods and the likelihood of securing mergers and acquisitions has become imperative because establishing that relationship may minimize the challenges faced by startups’ owners and investors during the negotiation process for mergers and acquisitions.
Research Questions and Hypotheses
The purpose of this quantitative survey study was to examine the effect of valuation methods on the likelihood of mergers and acquisitions of high-tech startup organizations in the
Nigerian capital market. This study was guided by the following research question and hypotheses:
6 RQ: To what extent, if any, does valuation method impact the likelihood of securing
funds through mergers and acquisitions for high-tech startup organizations?
Ho : There is no statistically significant probability of impact of valuation methods on the
ability of high-tech startups to secure mergers and acquisitions
Ha : There is a statistically significant probability of impact of valuation methods on the
ability of high-tech startups to secure mergers and acquisitions
In the hypothesis, the dependent variable was the likelihood of mergers and acquisitions of high-tech startups. The independent variable was the valuation methods used for measuring the value of high-tech startups. Typically, in Nigeria, the traditional valuation methods are frequently used in evaluating the economic value of the owner’s interest in an enterprise (Okafor
& Onwumere, 2012). The traditional valuation methods, as put forward by Stevenson, Roberts, and Grousebeck (1989), can be categorized into asset-based method; income/earnings-based method, cash-flow discounting method, and the market-based valuation method. Since valuation is fundamental to accessing finance, it is important to understand what constitutes the value of a firm and how that value is computed. Valuation is a necessary step in the decision to reconstruct, sell, merge, or acquire other businesses (Damodaran, 2012).
Söderblom, Samuelsson, and Mårtensson (2013) identified six main business valuation techniques in emerging markets: discounted cash-flow method (DCF), asset-based method
(ABM), earnings multiple methods, real options valuation model (ROVM), venture capital model (VCM), and the relative valuation method (RVM). In their work on valuing companies in emerging market using Nigeria as a case study, Aidamenbor and Mgbemena (2008) tested the popularity of valuation methods in emerging market as espoused in (Pereiro, 2002). The result
7 showed that the asset-based method, discounted cash-flows (DCF) method, income-based method, the earnings multiple method, the real option (option pricing) method, the market-based method and the economic value added (EVA) method were the most popular with corporations, financial advisors, banks, insurance companies, and individual investors. For this study, and in line with (Aidamenbor & Mgbemena, 2008; Söderblom, Samuelsson & Mårtensson, 2013), the following methods were discussed: asset-based method, discounted cash flows method, earnings multiple methods, real options pricing model, venture capital model, and the relative valuation.
Also, a mixed valuation method was added for a possible combination of two or more methods and a group for other valuation methods not highlighted in this study.
Theoretical Framework for the Study
The model theory for this study is the mergers and acquisitions theory (Akerlof, 1970;
Posner, 1981; Schendel, Patton, & Riggs, 1976) and supported by asset valuation methods that are components of valuation theory (Modigliani & Miller, 1958). Broadly, mergers and acquisitions theory can be described as activities involving corporate restructuring or changes in the ownership structure of firms (Rao & Kumar, 2013). The mergers and acquisitions theory is based on the assumption that benefits derived from mergers and acquisitions stem from the complementarities between acquiring and target firm’s assets and capital reallocation (Malik,
2014). The mergers and acquisitions theories relevant to this study were classified into three categories: wealth maximization theory (Posner, 1981); the turnaround theory (Barker &
Duhaime, 1997; Schendel, Patton, & Riggs, 1976) and the information asymmetry theory
(Akerlof, 1970; Spence, 1973; Stiglitz, 1975).
8 The mergers and acquisitions theories cited above formed the theoretical basis for the study and are discussed in more detail later in the study. This study was based on existing business valuation methods supported by mergers and acquisitions theories which provided a context for answering the research question. In this study, I argued that valuation methods were associated with the probability of startups securing funds via mergers and acquisitions. This was based on the assumption that misvaluation affects mergers and acquisitions (Pereiro, 2016).
Reddy (2014) observed that corporate growth strategies, such as mergers and acquisitions, leveraged buyouts, takeovers and other strategic alliances, have a significant implication for a firm’s growth prospects. However, misvaluation has been acknowledged as a major challenge when negotiating mergers and acquisitions (Pereiro, 2016). Rhodes-Kropf,
Robinson, and Viswanathan (2005) studied the effect of misvaluation on the chances of consummating mergers and acquisitions. Rhodes-Kropf et al. (2005) established that misvaluation affects who buys whom, the mode of payment, and provides explanations for neoclassical mergers and acquisitions activities.
Prior studies such as Damodaran (2012) examined different valuation models for startups while other studies discussed in broad terms the mergers and acquisitions of startup firms (Gao,
2015; Rok, 2012). However, a gap exists in the body of literature regarding the relatedness of valuation methods to the consummation of mergers and acquisitions. Similar to the research conducted by Fernández (2007a), a comparison of the various valuation methods and how they relate to securing funding through mergers and acquisitions were discussed. Also, the study includes other valuation models that involve multiple valuation processes similar to the research conducted by (Fiorentino & Garzella, 2014).
9 Valuation determines the worth of an asset and provides an agreeable price in which an offer and acceptance can be made (Mohammad, 2016). The traditional valuation methods, such as the DCF, CAPM, ABM, VCM, ROVM, and RVM were used as a theoretical base for discussing valuation methods for startup. The wealth maximization theory (Garzella &
Fiorentino, 2014), turnaround theory (Barker & Duhaime, 1997; Schendel, Patton & Riggs,
1976) and information asymmetry theory (Akerlof, 1970) were used to provide the theoretical framework for analyzing mergers and acquisitions as they relate to startups in line with the procedures set by (Karpoff, Lee, & Masulis, 2013).
The binary logistic regression model was used to assess the effect of valuation methods on the likelihood of securing funds via mergers and acquisitions for high-tech startups. Field
(2014) stated that the binary logistic regression model could be used to conduct regression analysis when the dependent variable was dichotomous. As a predictive analysis model, the logistic regression model can be used to predict the relationship between one dependent binary variable and one or more metric independent variables (Field, 2014).
In this study, the logistic regression model was used to establish the relationship between the independent variables and the likelihood of the dependent variable (Feng, 2016; Ngugi,
2014). The outcome of this analysis helped to describe the association between various valuation methods and the mergers and acquisitions of high-tech startup organizations.
Nature of the Study
This quantitative survey study was designed to examine the effect of valuation methods on the likelihood of mergers and acquisitions of high-tech startup organizations in the Nigerian
10 capital market. The goal was to assess whether valuation methods influence the chance of securing funds through mergers and acquisitions.
The choice of quantitative survey research for this study was appropriate. A survey research design is best used to draw statistical inferences from a large sample that is representative of the intended population when using other research designs might prove difficult
(Aggarwal & Saxena, 2012). Additionally, the quantitative survey research design allows for the combination of both observed and latent variables obtained during the data collection process
(Iverson, 2016). The dependent variable was the likelihood of mergers and acquisitions while the independent variable were the different valuation methods used in valuing high-tech startups organizations. Identifying the possible relationship between the two variables was important to this study. To test the hypotheses, survey data were obtained from 106 participating companies randomly selected from about 450 high-tech startups that participated in mergers and acquisitions activities between the years of 2006 and 2016. Senior executives of these companies were invited to participate in the survey. The survey data were collected electronically.
Data were collected through a random sampling of the high-tech firms involved in mergers and acquisitions between 2006 and 2016. The flexible statistical power analysis program, G*Power 3.1, was used to determine the minimum sample size. Details of this analysis are given in Chapter 3. This study was an attempt to determine the association between valuation methods and the chance of securing funds through mergers and acquisitions for startups in the high-tech industry. Establishing the relationship between valuation methods and the likelihood of mergers and acquisitions of high-tech startups could help to reduce valuation challenges of high-tech startup organizations in Nigeria and also expand the literature in this field.
11 Definitions
Capital asset pricing model : The CAPM is the foundation of all asset pricing theories, and probably one of the most fundamental theories in asset pricing. CAPM is useful in determining the relationship between risk and return, and helps in identifying undervalued or overvalued assets (Bajpai & Sharma, 2015). CAPM is best used in estimating a firm’s cost of equity. Equity cost of capital is equal to the return on a risk-free investment plus a premium which reflects the risk of the company’s equity (Akpo, Hassan & Esuike, 2015). The equation for
CAPM is given as:
(1) = + ∗ −
Venture capitals : Venture capitals are a section of the private equity industry which specializes in building high-risk startups with potentials for high growth (Festel, Wuermseher & Cattaneo,
2013). Venture capitals provide funding to startups in exchange for equity in that company.
Venture capital method: Venture capital method is a specialized valuation process in which venture capitalists integrates the features of DCF and the Multiples methods in determining the value of a young firm (Aydın, 2015).
Discounted cash flow method: The discounted cash flow method is used for estimating the value of a business based on the concept of time value of money (Bilych, 2013). The present value of the company is determined by discounting the projected cash flows of the business using the company’s appropriate cost of capital (Investopedia, 2017a).
Adjusted net asset method : The adjusted net asset of a company is determined by taking into consideration all the fixed assets and intangible assets owned by the company and adjusting for its obligations including off-balance sheet liabilities (Janas, 2013).
12 High-tech company: A high-tech company is characterized for being innovative in the use of cutting edge technology to deliver goods and services (Koller, Goedhart, & Wessels, 2010).
Relative valuation method : The relative valuation method is the process used to determine a company’s value by comparing the entity’s assets with identical assets for which the price is available (IVS 105, 2015).
Real option valuation : Real options valuation is used to determine the value of an investment usually by taking into consideration the real option available to the project such abandoning, expanding or deferring to undertake certain investment decisions (Investopedia, 2017b).
Book value : Book value refers to the theoretical value of a company’s net assets (Chandrapala,
2013). Theoretically, the book value of a company is equivalent to the amount of cash shareholders would receive if all of the company’s debt obligations were paid off and all remaining assets were sold. Chandrapala (2013) noted that no quality enterprise should sell for a price equivalent to or less than its theoretical liquidation value.
Valuation theory: The valuation theory as propounded by Modigiliani and Miller (1958) and
Black and Scholes (1973) is based on the assumption that arbitrage opportunities do not exist under equilibrium condition. The valuation methods used in this study are components of the valuation theory (Damodaran, 2005).
Pre-money vs. Post-money valuation : Pre-money refers to the value of a company before receiving financial support. Post-money valuation = pre-money valuation + new funding
(Damodaran, 2012). These terms are important in determining the ownership stake that will be given up during the funding round.
13 Operating cash flow : According to Bingilar and Oyadonghan (2014), operating cash flow measures the net cash flows from the operations of the business, not from sales of company assets (investing activities) or issuance of debt (financing activities). Operating cash flow can also be computed as the net operating income (NOI) + depreciation.
Leveraged buyout : Leveraged buyouts enable companies to make large acquisitions without committing huge capital. The target company's assets or revenue is used as "leverage" to pay back the amount used to acquire the company. Leveraged Buyout can also be referred to as
Highly-Leveraged Transaction (HLT) or bootstrap transaction (Tripathi, 2012).
Merger: In mergers, two or more firms combine to form a separate legal entity (Piesse, Lee, Lin,
& Kuo, 2013).
Acquisition : Acquisition is defined as activities by which a firm acquires more than 50% equity control of the acquired firms, whereas, in mergers, two or more firms combine to form a separate legal entity (Piesse, Lee, Lin, & Kuo, 2013).
Mergers and acquisitions: Mergers and acquisitions is a strategic expansion activity taken to transfer ownership of a company between two set of shareholders (Ross, Westerfield, & Jaffe,
2010). Mergers and acquisitions is seen as a fundamental strategy used for corporate restructuring and control as well as achieving growth strategy (Garzella & Fiorentino, 2014).
Information asymmetry : The concept of information asymmetry in corporate finance assumes that at least one party to a transaction has more useful information that might tilt the balance of power in the transaction (Salehi, Rostami, & Hesari, 2014). In other words, information is distributed asymmetrically between firm owners and investors (Stiglitz, 2000).
14 Valuing startups : Startup valuation refers to the process of determining the value of startups before it begins to generate revenues for the purposes of fund raising or investment (Damodaran,
2012).
Entrepreneur : An entrepreneur is described as one who undertakes new business activities and assumes the risks of the business despite the financial constraints associated with starting a new business (Amolo & Migiro, 2014).
Valuation : Valuation is the current worth of an asset or a company which is usually determined by a professional valuer (Investopedia, 2017c).
Initial public offering : An IPO involves selling a company’s shares to the general public on the stock market for the first time, which also serves as a means of obtaining financing for its projects (Hejazi, Salehi, & Haghbin, 2010). Through IPOs, companies raise equity (Chang-Yi,
Jean & Shiow-Ying, 2013) which provides investors with a liquid security with an established market price (Boeh & Southam, 2011).
Assumptions
The following assumptions were based on scientific realism and considered necessary for this study:
1. Survey respondents will provide honest and unbiased responses.
2. The effect of valuation methods on the likelihood mergers and acquisitions of high-tech
startups can be logically determined.
3. The logit model will fit correctly with the variables in the study.
15 Scope and Delimitations
The purpose of this quantitative survey study was to examine the effect of valuation methods on the likelihood of mergers and acquisitions of high-tech startup organizations in the
Nigerian capital market. The scope of this study was limited to startups in the high-tech sector of the Nigerian capital market. Considering the funding issues and the challenges of valuing startups, I sought to investigate the possible association of valuation methods for valuing startup and the mergers and acquisitions of high-tech startup organizations. The companies surveyed had been involved in mergers and acquisitions within the sampling period of 2006 and 2016. The sample included internet service providers (ISPs), telecommunication companies, IT infrastructural companies, finance and e-commerce, agriculture, health information technology, mobile application and engineering and this is consistent with the definition of high-tech companies provided by (Koller, Goedhart, & Wessels, 2010).
Therefore, the survey was limited to startups rendering high-tech services. Since the focus was on valuation methods and mergers and acquisitions, discussions on mergers and acquisitions, types, and processes featured prominently. To determine this relationship, survey data were obtained from 106 companies from among the 450 startups involved in mergers and acquisitions activities in the industry between the years of 2006 and 2016. These companies had to be registered with the corporate affairs commission.
Data obtained were recorded using questionnaires. The questionnaires were pretested and assigned unique identifiers to enhance the validity of the test instrument (Selltiz, Wrightsman, &
Cook, 1976). Before conducting the survey, a field test was conducted to include the inputs of practitioners in the survey. The sample size of 106 was appropriate in the circumstance given the
16 tremendous research work usually associated with handling high-tech startups’ related data and the need to establish the underlying pattern in the data. Research data were widely used in the study because of its suitability for a social scientific inquiry (Singleton & Straits, 2009).
Considering the scope and purpose of this study, little or no effort was made to discuss the historical perspective of mergers and acquisitions, the impact of acquisition premiums on the outcome of mergers and acquisitions nor anti-takeover mechanisms. In addition, the impact of the years of operation on the valuation of startups was not part of this study. Therefore, there is likelihood that the non-inclusion of these variables in the logistic regression may have affected the statistical inference drawn from the study (Karlson, Holm, & Breen, 2012). Survey research designs are usually characterized with issues of low response rate and biased responses (Iverson,
2016). Thus, the extent to which the findings of this study could be generalized may be impacted.
Limitations
This study was subject to four limitations. The first may be traced to the methodology.
The major limitation of cross-sectional studies is the inability to establish causation (Altman &
Krzywinski, 2015; Barrowman, 2014; Deilami, Hayes & Kamruzzaman, 2016; Eaves, Hatemi &
Verhulst, 2012). Therefore, only an association of valuation methods and the likelihood of mergers and acquisitions can be inferred. The inability to determine causation limits the likelihood that the outcome of this study may establish whether valuation methods prevent mergers and acquisitions.
A second limitation may be traced to the narrow sample base and the inability to achieve
100% of the expected sample size (see Chapter 4 for details). The inability to achieve the desired
17 sample size could impact the generalization of the survey’s result to a larger population.
Insufficient data imply that only available data was used for the study. This challenge may limit the outcome of this study to be generalized to other sectors of the economy. Nonetheless, the impact of this on the outcome of the study was limited because of the use of random sampling.
Also, 96.0% of the expected sample size was achieved despite being a niche sample and exceeding the sample to variable rule of 10 (Austin & Steyerberg, 2015).
Inability to account for confounding variables in the regression equation was a third limitation of the study. The non-inclusion of spurious factors such as nature, size, and age of the company in the model may have affected the results of the study (Neuman, 2011).
A fourth limitation was the validity of the constructs. Details of how the constructs were operationalized are given in Chapter 3. Construct validity has to do with the adequacy of the operational definition and measurement of the theoretical constructs (Martin, Cohen, &
Champion, 2013). To reduce the impact of construct validity on the study, the questionnaires were pretested with panel of experts reviewing the theoretical constructs with a view to reducing the instrumentation issues before implementation (Archibald, 2016; Bolarinwa, 2015; Zohrabi,
2013).
Significance of the Study
Contribution to Theory
This study is expected to contribute to the ease of valuing startups and extend the existing literature on the startup phenomenon. Researchers may build on the knowledge gained in this study to expand the startup phenomenon especially as it relates to evaluation, funding, and opportunities for mergers and acquisitions of high-tech startups. In this study, I extended the
18 discussion beyond the conventional traditional valuation methods to evaluating how a combination of different valuation methods may be used to more accurately determine if funding can be secured through mergers and acquisitions for high-tech startups.
Stakeholders such as financial analysts, researchers, entrepreneurs, investors, and regulators, who depend on accurate valuations, may benefit from improved valuation accuracy that this study provides (Reddy, Agrawal, & Nangia, 2013). Additionally, the study’s findings may lead to the intersubjectivity of valuation experts, accelerate evaluation processes, and reduce the factors militating against mergers and acquisitions in the high-tech industry in Nigeria. Since valuation is important to investors (Salehi, Rostami, & Hesari, 2014), the intrinsic value of growing firms may be easily determined (Coit, 2016). An understanding of the linkages between valuation methods and mergers and acquisitions might enable high-tech startups adopt the best valuation methods that brighten the prospect of mergers and acquisitions.
Contribution to Practice
The study is expected to contribute to practice as private equity firms, venture capitals, and other organizations saddled with valuing young firms may benefit from additional information about startup valuation presented. Further, the valuation challenges experienced by valuers of high-tech startups have been addressed by eliminating the guesswork usually associated with valuing startups (Monika, Nitu & Latika, 2013). This should help to bridge the expectation gaps between the founders and funders during the negotiation process. By aligning the ambitions of the entrepreneur and the investors, the rate of mergers and acquisitions of high- tech startups might be improved. With improved valuation procedures, stakeholders may be able to determine their interests in associated companies (Coit, 2016).
19 On the policy front, the study may be beneficial to the National Communications
Commission (NCC) and the Federal Ministry of Science and Technology in crafting policies that may support young entrepreneurs with innovative ideas to start their businesses. This may help to reduce the unemployment rate in Africa’s biggest economy. The Ministry of Labor and
Productivity, as well as the Lagos Chambers of Commerce and Industry (LCCI), may also benefit from the output of this study given its detailed coverage of high-tech startups especially in the economic diversification program of the government. The study was designed to address the gaps in literature that might have hindered access to the funding and growth of startups.
Implications for Social Change
The study is expected to contribute to positive social change by enhancing the knowledge base of entrepreneurs as well as investors. The study's findings may help to unlock access to funding and enhance the innovation and entrepreneurial tendencies of young Nigerians. The essence of creating organizations is to improve the wealth of their shareholders (Gómez-Bezares,
Przychodzen & Przychodzen, 2016). Shareholders may benefit from improved valuation techniques and an increased aggregate net worth, which the findings of this study reveal. The study’s outcome may help to reduce unemployment rate within the country as more people take up to entrepreneurship because of the increased knowledge about asset valuation, and access to funding and exit strategies. Thus, once organizations become successful, they are able to perform their functions to society such as job creation, provision of social amenities, contributions to innovation, and continued infrastructural and social development of the communities in which they do business (Hollensbe, Wookey, Hickey, George & Nichols, 2014).
20 Summary
Valuing high-tech startup organizations is fast becoming more an art than a science.
Financial managers seeking to create firm value need to make smarter investment decisions armed with valuation techniques appropriate for different buckets of investments. Traditionally, firms are assessed using the DCF method, market approach, income approach or cost approach.
Following this line of thinking, I examined how best to use valuation methods to value startups.
Researchers have demonstrated that using conventional valuation methods to evaluate startups yield subjective results and are susceptible to market failures (Paternò-Ca stello, Vezzani, Hervás
& Montresor, 2014). Essentially, not all values are equal; a clear understanding of what value is being measured is important along with the purpose for which it is measured. As a result, determining the appropriate value to measure is crucial when taking investment decisions.
Startups by their nature are constrained by financing, intellectual capacity, political and policy instability, and risk management problems (Katua, 2014). Incorporating mergers and acquisitions into the growth strategy mix should play a major role in addressing these challenges and might accelerate the transitioning phase of high-tech startups. This is the difference between organic and inorganic growth (Satnalika, 2016; Sharma, 2015). In addition, being open to the use of different valuation methods or a combination of methods reduces the valuation crisis during a merger and acquisition process (Mangipudi, Subramanian, & Vasu, 2013). By reviewing various works of literature as provided in Chapter 2, I provide an analysis of the economic realities of growing firms as well as the valuation challenges faced by founders as well as funders. This study was undertaken to identify the interactions between valuation methods and the mergers and acquisitions of high-tech startup organizations.
21 To achieve this objective, contemporary literature on valuation of startups, valuation methods, and mergers and acquisitions processes are reviewed in greater detail in Chapter 2. The impact of firm valuation methods on the success of mergers and acquisitions documented in prior studies are also reviewed. Chapter 3 is dedicated to discussion of the research methodology, the theoretical framework, and the research hypotheses. A detailed description of the dependent and independent variables, together with data collection procedures and interpretation are presented in Chapter 4. Finally, Chapter 5 includes the summary of the study, conclusions, and recommendations.
22 Chapter 2: Literature Review
The purpose of this quantitative survey study was to examine the effect of valuation methods on the likelihood of mergers and acquisitions of high-tech startup organizations in the
Nigerian capital market. This study was designed to address the funding challenges usually associated with high-tech startups. The overarching intention of this study was to leverage existing literature as well as field analysis to advance an external growth strategy for startups in the high-tech sector. As such, I examined the impact of valuation methods on the likelihood of securing funds through mergers and acquisitions for high-tech startups in the Nigerian capital market.
In this section, I build on the existing literature to highlight the historical perspective of the startup phenomenon in Nigeria and the life-cycle model used for charting the growth trajectory of growing firms. The theoretical foundation on which this study is hinged was also discussed. This includes the valuation methods for high-tech startups and the governing theories for mergers and acquisitions related to the study. Also discussed were the motives for mergers and acquisitions and the typical mergers and acquisitions process.
Historical Perspective of the Startup Phenomenon
Before examining the existing literature, I provide a historical perspective of the evolution of startups (Simon, 1993) and mergers and acquisitions. Although history is replete with the evolution of small businesses, archivists have little to report on the development of high-tech startups due to the frequency of disruptive technologies in that space. Jaafar and Abdul
Halim (2016) attributed the dearth of empirical studies to the historical perspective of high-tech companies and the difficulties in operationalizing the life-cycle concept of high-tech startups.
23 Small businesses dominate the Nigerian economic landscape and have been a part of the society with vibrant entrepreneurial skills predating colonial times. Entrepreneurship is synonymous with small businesses, and it began when people produced more products than they required and had to swap the surplus (Raymond, Moses, Ezenyirimba & Otugo, 2014). The example of this can be seen when blacksmiths produced more farm implements, such as hoes and cutlasses, than needed and had to exchange them with farmers for goats, yams or other foods
(Ebiringa, 2011). Before money became legal tender, this phenomenon was widely referred to as
“trade by barter.”
Modern entrepreneurial activities began in Nigeria with the coming of the colonial masters, who brought in their wares in exchange for commodities or other local materials. The adoption of the Structural Adjustment Programme (SAP) in 1986 by the administration led by
General Babangida resulted in dramatic policy shift from capital-intensive and large-scale corporations to SMEs (Ebiringa, 2011). The quest for growth, market control, and wealth maximization led to the idea of business combinations in the form of mergers and acquisitions
(Garzella & Fiorentino, 2014). Malik, Anuar, Khan and Khan (2014) discussed the six waves of mergers and acquisitions that occurred between 1897 and 1981. In Nigeria, the first wave of mergers occurred after the Nigerian civil war (1967-1970) and gained momentum after the failure of banks in the 1990s, which triggered more mergers and acquisitions opportunities in
Nigeria (Ebimobowei & Sophia, 2011). The highest number of mergers and acquisitions has been recorded in the financial services sector, especially in the banking subsector (Ebimobowei
& Sophia, 2011).
24 Formation and Development of Startups
Researchers have utilized Erikson’s (1963) organizational life cycle model to explain the transition of firms from initial entrepreneurial phase to a later phase that is more complex and sophisticated, requiring a bureaucratic type of management systems (Shahmansouri & Nazari,
2013). Life-cycle theory assumes the growth process of startups follow predictable and sequential patterns (Smith, Mitchell, & Summer, 1985). The life-cycle is commonly used by scholars to illustrate the progression of firms through growth stages. The development of startups is classified into different growth stages based on the problems they encounter in the growth process. Table 1 depicts the development of startups in line with the life-cycle model
25 Table 1 Life Cycle Model
Model Theorists Life cycle content
3-stage Bhave (1994) a. Opportunity stage b. Technology setup and organization stage c. Exchange stage
4-stage Kazanjian (1988) a. Conception and development
b. Commercialization c. Growth
d. Stability
5-stage Galbraith (1982) a. Proof of Anthony and Ramesh (1992) principle/Prototype stage b. Model shop c. Start-up
d. Natural growth
e. Strategic maneuvering
10-stage Block and a. Development of concept, MacMillan (1985) completion of product testing
b. Completion of product prototype
c. Initial financing
d. Completion of initial plant testing
e. Market testing
f. First batch production
g. Early sales
h. First competitive activities
i. First redesign or adjustment of direction j. First major adjustment of prices
Note. Adopted from “Development of a startup business: A complexity theory perspective” by S. D, Tsai, and T. T, Lan, 2006, National Sun Yat-Sen University , Kaohsiung, Taiwan pp. 4-5.
26 Furthermore, in Chapter 2, I provided a detailed review of existing literature on high-tech startups, valuation methods, and mergers and acquisitions and how they affect the activities of startups. This chapter is organized into five different sections. The first section comprised of various valuation methods used for valuing startups, related theories, strengths and weaknesses of each method and related concepts. A detailed description of the theories on acquisition dynamics in mergers and acquisitions is provided in the second section. The third section includes the literature review on the motives for mergers and acquisitions and the implication of mergers and acquisitions on the ownership structure of startups. Provided in the fourth section, is the analysis of the different opinions of researchers on the impact of mergers and acquisitions on growing firms, thus bringing the dependent and independent variables together.
Literature Search Strategy
This study was largely dependent on publicly accessible sources as well as field reports.
The databases consulted included Google Scholar, Social Science Research Network (SSRN),
JSTOR, Sage Premier and Science Direct. News releases from regulatory institutions such the
Central Bank of Nigeria (CBN), Nigerian Stock Exchange (NSE), Securities and Exchange
Commission (SEC) formed part of the resources utilized in this study. Others include releases from the National Bureau of Statistics (NBS) and the Lagos Chambers of Commerce and
Industry (LCCI).
The search period covered a period of 2008 and 2017; however, older articles were collected in order to highlight base theories and where they fundamentally provide depth about the subject matter. The search was limited to only peer-reviewed articles published between 2008 and 2017. Emphasis was placed on original sources for primary referencing and for additional
27 references. The following keywords were used in the study: mergers and acquisitions, bootstrap acquisition, hostile takeover, cost per acquisition, poison pill, valuation methods, high-tech startups, small and medium scale of enterprises, innovative potentials of start-ups, capital asset pricing model, venture capitals, private equity firms, firm performance, internal and external growth factors, initial public offering, asset pricing, information asymmetry, auction theory, acquisition theory, decision theory, complexity theory, management theory, life cycle theory,
Lean start-up, book value and market value, market for lemons, discounted cash flow, risk free rate, free cash flow hypothesis, managerial self-interest hypothesis, private benefits hypothesis, capital investment and investment strategy, financial performance, analysis of variance, multivariate analysis, logistic regression, binomial logistic regression and forecasting .
Theoretical Foundation
The model theory for this study is the mergers and acquisitions theory propounded by
(Akerlof, 1970; Posner, 1981; Schendel, Patton, & Riggs, 1976) and supported by asset valuation methods which are components of the valuation theory of (Fisher, 1930; Modigliani & Miller,
1958; Williams, 1938). The mergers and acquisitions theory, as well as the asset valuation methods, were selected because the mergers and acquisitions theory enables me to describe how startups can achieve organizational goals through external financing while the asset valuation principles are necessary to establish the worth of the business.
The mergers and acquisitions theory is based on the assumption that benefits derived from mergers and acquisitions stem from the complementarities between acquiring and target firm’s assets and capital reallocation (Malik, 2014). The mergers and acquisitions theories relevant to this study can be classified into three categories: wealth maximization theory (Posner,
28 1981); the turnaround theory (Barker & Duhaime, 1997; Schendel, Patton, & Riggs, 1976) and the information asymmetry theory (Akerlof, 1970; Spence, 1973; Stiglitz, 1975). In line with the procedures set by (Karpoff, Lee, & Masulis, 2013), I utilized the mergers and acquisitions theories to explain the dependent variable especially as they relate to startups.
Existing business valuation methods were used in conjunction with the mergers and acquisitions theories to provide a context for answering the research question. In this study, I argued that valuation methods predict the likelihood of securing funds via mergers and acquisitions for startups. This is based on the assumption that misvaluation affects mergers and acquisitions (Pereiro, 2016).
Determining the value of an asset is crucial in arriving at an agreeable price in which an offer and acceptance can be made (Mohammad, 2016). Establishing the value of an entity such as high-tech startups is a complicated process in which different valuation methods may need to be employed to determine its fair value or market value (Xiangying, Yueyan, & Xianhua, 2015).
An entity’s value consists of all is assets and liabilities and its capacity to generate future economic benefits (Ghiță-Mitrescu & Duhnea, 2016). The International Financial Reporting
Standard (IFRS 13) described the fair value of an asset as the “price that would be received to sell an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date” (IFRS, 2013, p. 62).
At different times, theorists have contributed to the evolution of the various valuation methods used today. Valuation principles, such as the discounted cash flow methods (Fisher,
1930; & Williams, 1938), capital asset pricing model (Lintner, 1965; Mossin, 1966; Sharpe,
1964), asset-based method (Lee, 1996; Reilly & Schweihs, 1999), venture capital method
29 (Sahlman, 1990), real options pricing model (Myers, 1987), and the relative valuation model
(Damodaran, 2001) were used as the theoretical base for discussing valuation methods for high- tech startup as well as to establish the market price for startups. Also included, are other valuation models that involve multiple valuation processes similar to the research conducted by
(Fiorentino & Garzella, 2014).
The mergers and acquisitions theories especially as they relate to the growth of firms have been studied extensively. Rhodes–Kropf, Robinson, and Viswanathan (2005) studied the effect of misvaluation on the chances of consummating mergers and acquisitions transactions.
Rhodes–Kropf et al. (2005) established that misvaluation affects who buys whom, the mode of payment, and provides explanations for neoclassical mergers and acquisitions activities. Reddy
(2014) identified that corporate growth strategies such as mergers and acquisitions, leveraged buyouts, takeovers and other strategic alliances have a significant impact on the growth prospects of companies. In another related study, Fernández (2007a) discussed the various valuation methods and how they relate to securing funding through mergers and acquisitions, and
DePamphilis (2014) discussed the different stages involved in mergers and acquisitions.
Prior studies such as (Damodaran, 2012; DePamphilis, 2014) examined different valuation methods for startups while other studies have discussed in broad terms the valuation methods used in the mergers and acquisitions of startup firms (Gao, 2015; Rok, 2012) which is similar to the ones used in this study. Miloud, Aspelund, and Cabrol (2012) conducted an empirical study on the valuation methods used by venture capitalists. They also investigated what venture capitalists consider when estimating the value of startups. Miloud, Aspelund, and
Cabrol (2012) identified industry attractiveness, quality of the owner, the composition of the
30 management team, as well as external relationships as significant to establishing the value of a
startup seeking venture capital financing.
Literature Review Part 1: Startup Valuation Theories
Relative Capital Asset Discounted Asset-Based Venture Real Option Cash Flow Valuation Valuation Pricing Model Capital Model Pricing Model Model Method Model
Adjusted Net Price-to- Single Factor Free Cash Expected Financial Asset Earnings Model Flow to Firm Returns Options Approach Ratio
Multi-Factor Free Cash Flow to EBITDA Exit Value Real Options /APT Model Equity
Call & Put Options
FigureFigure 1. 1:Business Business valuation valuation models. models
Capital Asset Pricing Model
The capital asset pricing model (CAPM) which was introduced by (Lintner, 1965;
Mossin, 1966; Sharpe, 1964) is one of the most widely used asset pricing tool today in modern
finance and has applications in asset valuation, portfolio performance measurement and portfolio
risk management (Pavlikov & Uryasev, 2014). Following Markowitz’s (1952) work on
diversification and modern portfolio theory, theorists (Lintner, 1965; Mossin, 1966; Sharpe,
31 1964) propounded the capital asset pricing model in which they found that return on an individual asset, or a group of assets, should be equal to its cost of capital. The model is particularly useful in determining the required rate of return of an asset, and it provides a theoretical basis for estimating the price of an asset using the firm’s expected cash flow
(Elbannan, 2015). Thus, the CAPM is not a standalone valuation method; however, CAPM is used to determine the required cost of capital when making an investment decision which can then be applied to evaluate the value of a company using the discounted cash flow method
(Elbannan, 2015). The CAPM posits that investors are compensated for the time value of their investment and any possible risk incurred during the investment (Dawson, 2015).
Sharpe (1964) built on the mean-variance optimization of portfolio articulated by
Markowitz (1959) and explains that return premium of any financial asset over the risk-free return is directly proportional to the systematic or non-diversifiable risk of the given asset (Karki
& Ghimire, 2016). During the last four decades, the CAPM has been one of the most widely used asset valuation techniques; it has been the benchmark of asset pricing models and has been used by most researchers to estimate the return on assets and the cost of capital (Shih, Chen, Lee &
Chen, 2014).
Shih, Chen, Lee and Chen (2014) described the assumptions guiding the use of CAPM as follows:
1. Investors are risk-averse who seek to maximize the expected utility of their wealth;
2. Investors are price takers and have homogeneous expectations about asset returns that
both have normal distribution;
32 3. The existence of a risk-free asset from which investors may borrow or lend unlimited
amounts at a risk-free rate;
4. The quantities of assets are fixed, and all assets are tradable and perfectly divisible.
5. Asset markets are frictionless, and the information is costless and simultaneously
available to all investors
6. Market imperfections such as taxes, regulations, transaction cost or restrictions on
short selling do not exist.
According to Sharpe (1964) and Lintner (1965), the CAPM can be mathematically expressed as:
(2) = + −
The market Beta can be determined as follows: